CN112034731A - Payload semi-physical simulation system based on associated knowledge - Google Patents
Payload semi-physical simulation system based on associated knowledge Download PDFInfo
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Abstract
The invention discloses a payload semi-physical simulation system based on associated knowledge, which comprises: the system comprises a telemetry data content simulation module, a telemetry data format simulation module, a load physical interface simulation module and a simulation platform module; the telemetering data content simulation module is used for dynamically generating telemetering original data which accords with the current working state of the load equipment; the telemetering data format simulation module is used for carrying out source packet format packaging on telemetering original data to generate load telemetering source packet data; the load physical interface simulation module is used for sending load telemetering source packet data to the tested equipment through the satellite bus physical board card and receiving a data injection instruction, a remote control instruction and broadcast time code information sent by the tested equipment; and the simulation platform module is used for managing the telemetry data content simulation module, the telemetry data format simulation module and the load physical interface simulation module to complete data exchange and matching cooperation among the modules.
Description
Technical Field
The invention relates to the technical field of ground comprehensive test of an effective load subsystem, single machine test of a load manager, ground comprehensive test of a satellite system and the like, in particular to an effective load semi-physical simulation system based on associated knowledge.
Background
The method integrates a plurality of payload devices and a load manager to complete the ground comprehensive test of a payload subsystem, further integrates the payload subsystem with other subsystems such as satellite comprehensive electronics and the like to complete the ground comprehensive test of a satellite system, and is a main technical approach for verifying the overall design and the interface design of a satellite and a payload.
The method is an effective method for verifying the overall design, the interface design and the reliability of the satellite and the effective load in time and improving the reliability of a spacecraft system.
The traditional load semi-physical simulation system effectively solves the problem of equivalent simulation of a physical interface of load equipment, but still the simulation of a load data interface is still in a simulation layer of a data format and a data protocol, the simulated data content is not related to the telemetering parameter design, the remote control instruction design, the health management strategy design of the effective load, the working mode design of matching and cooperation among the equipment and the like, and the working mechanism and the dynamic working process of the effective load equipment cannot be reflected from the data perspective. Based on the requirement of a ground comprehensive test system for testing coverage of effective load design knowledge, the simulation of a load data interface is expanded to a data content level from the existing data format and data protocol level, and the trend is formed.
The associated knowledge of the payload data refers to: the method comprises the following steps that a telemetering parameter of the load equipment is subjected to value taking in different time periods, or between two or more telemetering parameters, and certain association exists between an injection instruction and the telemetering parameter value, between a health management strategy and the telemetering parameter value, and between equipment failure and the telemetering parameter value. The association truly reflects the design of telemetry parameters, remote control commands, health management strategies, and the design of working modes of matching cooperation among devices of satellites and payload devices.
Disclosure of Invention
The invention aims to solve the problem that simulation telemetering data cannot dynamically reflect the working mechanism and working process of payload equipment in a traditional payload semi-physical simulation system, and provides a payload semi-physical simulation system based on association knowledge.
In order to achieve the above object, the present invention provides a payload semi-physical simulation system based on associative knowledge, wherein the system comprises: the system comprises a telemetry data content simulation module, a telemetry data format simulation module, a load physical interface simulation module and a simulation platform module; wherein the content of the first and second substances,
the remote measuring data content simulation module is used for dynamically generating remote measuring original data which accord with the current working state of load equipment according to the received data injection instruction, the remote control instruction, the effective load remote measuring data association knowledge model and the load user test requirement;
the telemetering data format simulation module is used for carrying out source packet format packaging on telemetering original data based on a CCSDS protocol standard to generate load telemetering source packet data;
the load physical interface simulation module is used for sending load telemetering source packet data to the tested equipment through the satellite bus physical board card and receiving a data injection instruction, a remote control instruction and broadcast time code information sent by the tested equipment;
and the simulation platform module is used for managing the telemetry data content simulation module, the telemetry data format simulation module and the load physical interface simulation module to complete data exchange and matching cooperation among the modules.
As an improvement of the above system, the telemetry data content simulation module comprises: the system comprises a single telemetering variable associated knowledge simulation sub-module, a multi-telemetering variable associated knowledge simulation sub-module, an injection instruction and telemetering variable associated knowledge simulation sub-module, a health management strategy and telemetering variable associated knowledge simulation sub-module and a load fault and telemetering variable associated knowledge simulation sub-module; wherein the content of the first and second substances,
the single telemetering variable association knowledge simulation submodule is used for generating telemetering original data reflecting the association characteristics of the single telemetering variable according to an association knowledge model between values of the single telemetering variable at different moments;
the multi-telemetering variable association knowledge simulation submodule is used for generating telemetering original data reflecting the association characteristics of the multi-telemetering variables according to an association knowledge model of the plurality of telemetering variables between values at the same time or different times;
the injection instruction and telemetering variable association knowledge simulation submodule is used for simulating a telemetering variable which changes correspondingly under the excitation of an injection instruction and generating telemetering original data reflecting the execution state of a load instruction;
the health management strategy and telemetering variable association knowledge simulation submodule is used for generating telemetering original data triggering the load health management strategy according to an association knowledge model between the health management strategy and the telemetering variable;
and the load fault and telemetering variable association knowledge simulation submodule is used for generating telemetering original data triggering the load fault according to the association knowledge model between the fault and the telemetering variable.
As an improvement of the above system, the specific implementation process of the single telemetry variable association knowledge simulation submodule is as follows:
when the values of the telemetering variable Y to be simulated at different simulation moments t all belong to the enumerated value set VEnu＝{v1,…,vnIn the time of the previous step, generating telemetry raw data y (t) by adopting an enumeration association model:
y(t)＝α1v1+α2v2+…+αnvn
wherein alpha isiThe number of the coefficients is represented by,
and alpha isi∈{0,1}；
When the values of the remote measurement variable Y to be simulated at different simulation moments t all belong to a threshold value interval VThr＝[vmin,vmax]Generating telemetry raw data y (t) by using a threshold correlation model:
y(t)＝vmin+(vmax-vmin)
wherein, is a random number between 0 and 1, vmin,vmaxRespectively the minimum value and the maximum value of the threshold interval;
when the remote measuring variable Y to be simulated is at two adjacent simulation moments ti-1、tiAll the increments of the values belong to an enumeration set VEnu＝{v1，…，vnAt time, from Y at ti-1Value y (t) of simulation timei-1) Generating telemetry raw data y (t) by using an incremental enumeration association model:
y(t)＝{y(t0),…,y(t-1),y(ti)}
y(ti)-y(ti-1)＝α1v1+α2v2+…+αnvn
wherein, y (t)0) For an initial value of simulation, alphaiAs a function of the number of the coefficients,
and alpha isi∈{0,1}；
When the value of the telemetering variable Y to be simulated at different simulation moments t presents the correlation characteristics of the period l, generating telemetering original data Y (t) by adopting a period correlation model:
y(t)＝{y(t0)，…，y(tl)}
y(ti+nl)＝y(ti)
wherein, the integer i belongs to [0, l ], l is the period of the value of the telemetering variable, and n is an integer;
when the values of the telemetering variable Y to be simulated at different simulation moments t have the correlation characteristics of linear increment, decrement or invariance, generating telemetering original data Y (t) by adopting a trend correlation model:
y(t)＝kt+y(t0)
where k is the slope of a linear increment or decrement, y (t)0) Is an initial value of simulation.
As an improvement of the above system, the specific implementation process of the multi-telemetry variable association knowledge simulation submodule is as follows:
when n telemetric variables Y to be simulated1,…,YnWhen the linear correlation characteristic is provided, the linear correlation characteristic is obtained,
telemetering variable Y from the first n-11,…,Yn-1Value y at simulation time t1(t),…,yn-1(t) generating Y by a function correlation modelnTelemetry raw data y at simulation time tn(t)：
Wherein k is1,…,knN numbers not all being 0;
when the remote measurement variable Y is to be simulated2With another telemetric variable Y1When having a monotone correlation characteristic, by Y1At simulation time ti、tjValue t of1(ti)、t1(tj) Generating Y using a monotonic correlation model2At simulation time ti、tjLower telemetric raw data value y2(ti) And y2(tj)：
Wherein the content of the first and second substances,
greater than 0 represents Y1And Y2In order to be a monotonically positive association,
less than 0 represents Y1And Y2Is a monotonically negative association;
when the remote measurement variable Y is to be simulatednAnd n-1 telemetric variables Y1,…,Yn-1When having the function-related feature, is represented by Y1,…,Yn-1Value y of1(t),…,yn-1(t) generating Y by a function correlation modelnTelemetry raw data y at simulation time tn(t)：
yn(t)＝f(y1(t)，…,yn-1(t))
Wherein f represents n telemetry variables Y1，…，YnFunctional relationship between;
when a telemetric variable Y1Remote measurement variable Y to be simulated2When the effect of (a) is not to occur until after a certain time, from Y1Value y at simulation time t1(ti) Generating Y by using a time delay function correlation model2At simulation time tjRaw data y of telemetry data2(tj)：
y2(tj)＝f(y1(ti))
Wherein, tj>ti。
As an improvement of the above system, the specific implementation process of the injection instruction and telemetry variable association knowledge simulation submodule is as follows:
t after the load execution instruction c of the telemetric variable Y to be simulatedBAt that moment, the value of Y will change to the threshold interval VThr＝[vmin,vmax]Generating Y after the load execution instruction c t by using the interval correlation modelBTelemetric raw data y (c, t) at timeB)：
y(c,tB)＝vmin+(vmax-vmin)
Wherein is a random number between 0 and 1;
t after the load execution instruction c of the telemetric variable Y to be simulatedBAt that moment, the value of Y will change to a constant value vconGenerating Y by using a numerical correlation model after the load executes the instruction c and tBTelemetric raw data y (c, t) at timeB)：
y(c,tB)＝vcon；
When the remote measurement variable Y to be simulated is before and after the load execution instruction c, the difference between the values of the Y is a constant value vconThen instruction c is executed by Y before tFThe value y (c, t) of timeF) Generating Y after the load execution instruction c t by adopting an incremental numerical value correlation modelBTelemetric raw data y (c, t) at timeB)：
y(c,tB)＝y(c,tF)+vcon；
When the remote measurement variable Y to be simulated is before and after the load execution instruction c, the difference of the values of Y belongs to a threshold interval VThr＝[vmin,vmax]Then instruction c is executed by Y before tFThe value y (c, t) of timeF) Generating T after load execution instruction c of Y by adopting increment interval association modelBTelemetric raw data y (c, t) at timeB)：
y(c,tB)＝y(c,tF)+vmin+(vmax-vmin)
Wherein, the random number is between 0 and 1.
As an improvement of the system, the specific implementation process of the health management strategy and telemetry variable association knowledge simulation submodule comprises the following steps: a continuous threshold correlation model and a multi-dimensional increment interval correlation model; wherein the content of the first and second substances,
when the value of the remote measurement variable Y to be simulated exceeds the threshold value for n times continuously, the correlation instruction c is automatically triggered to be executed, the remote measurement original data Y (t) of the Y is generated by adopting a continuous threshold value correlation model, and the n times are all larger than the threshold value;
when the remote measurement variable Y is to be simulated1The value corresponding to the simulation time t and another telemetering variable Y2The difference value of the corresponding values at the simulation time t belongs to a threshold interval VThr＝[vmin,vmax]Automatically triggering execution of the associated command c, then Y2Value y of2(t) generating Y by using a multidimensional incremental interval correlation model1Telemetering raw data y1(t)：
y1(t)＝vmin+(vmax-vmin)+y2(t)
Wherein, the random number is between 0 and 1.
As an improvement of the system, the specific implementation process of the load fault and telemetry variable association knowledge simulation submodule is as follows:
and modifying the telemetering original data generated by the single telemetering variable associated knowledge simulation submodule, the multi-telemetering variable associated knowledge simulation submodule, the injection instruction and telemetering variable associated knowledge simulation submodule and the health management strategy and telemetering variable associated knowledge simulation submodule to ensure that the modified telemetering original data does not meet the original associated knowledge model, thereby obtaining the simulated fault telemetering original data.
As an improvement of the system, the bus interface between the load physical interface simulation module and the satellite bus physical board card comprises a 1553B bus interface, a CAN bus interface and an RS422 bus interface.
Compared with the prior art, the invention has the advantages that:
the load telemetering data generated by the invention can practically reflect telemetering parameter design, remote control instruction design, health management strategy design and fault design of the payload equipment, and effectively solve the problem that the simulation telemetering data can not dynamically reflect the working mechanism and working process of the equipment in the traditional payload semi-physical simulation system.
Drawings
FIG. 1 is a block diagram of a payload semi-physical simulation system of the present invention;
FIG. 2 is a flow diagram of a payload semi-physical simulation system method of the present invention;
FIG. 3 is a diagram of a telemetry data content simulation module component of the present invention.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
FIG. 1 is a block diagram of a payload simulation system based on associative knowledge according to the present invention.
The system mainly comprises a telemetry data content simulation module, a telemetry data format simulation module, a load physical interface simulation module and a simulation platform module.
The functions of the modules are as follows:
1) the telemetry data content simulation module: generating telemetering original data which accords with the current working state of the load equipment according to a remote control instruction, a simulation moment, a load equipment operation mechanism and test requirements of a test task on a health management strategy and a load fault which are currently received by the effective load simulation equipment;
2) the telemetry data format simulation module: based on the CCSDS (coherent Committee for Space Data System, CCSDS) protocol standard, adding information such as a source packet header, a packet count and a head pointer to telemetering original Data generated by a telemetering Data content simulation module, and finishing the packaging of telemetering source packet Data;
3) load physical interface simulation module: by means of the 1553B, CAN and RS422 bus commercial board card, data communication with the tested equipment is completed through a 1553B bus interface (including two types of a terminal and a bus Controller), a CAN (Controller Area Network) and an RS422 bus interface. Sending a load telemetering source packet to the tested equipment, and receiving information such as a data injection instruction, a remote control instruction, a broadcast time code and the like sent by the tested equipment;
4) a simulation platform module: the method manages modules of telemetry data content simulation, telemetry data format simulation, load physical interface simulation and the like, and calls the functions of all the modules according to time sequence to complete data exchange and matching cooperation among the modules.
Fig. 2 shows a flow chart of the present invention.
Designing a telemetry data simulation module:
in view of the similarity between other modules and the existing load semi-physical simulation system, the design of other modules is not repeated herein, and only the design of the telemetry data simulation module is explained. The telemetry data simulation module is composed as shown in fig. 3 according to the data objects related by the related knowledge.
1. Telemetry data simulation submodule based on single telemetry variable association knowledge
The single telemetering variable association knowledge means that the value Y (t) of a single telemetering variable Y at different simulation moments t1),…,y(tn) The relationship between them.
1) Enumerating associations
Known enumeration set VEnu＝{v1,…,vnThe values Y (t) of the telemetry variable Y at any simulation time t belong to a set VEnuI.e., an enumerated association. The mathematical model is as follows:
y(t)＝α1v1+α2v2+…+αnvn
wherein the content of the first and second substances,
and alpha isi∈{0,1}。
The telemetric variables with enumeration association are load state quantity and switching value, including working mode of equipment, error code and enablingA logo, etc. Design values such as the telemetry variable "geomorphologic camera operating mode" belong to an enumeration set VEnuThe imaging mode includes {0x00, 0x01, 0x02}, where 0x00 represents no operation, 0x01 represents a still photography mode, and 0x02 represents a motion photography mode.
2) Threshold association
Known threshold interval VThr＝[vmin，vmax]The value Y (t) of the telemetric variable Y at any simulation time t belongs to the interval VThrI.e. is a threshold association. The mathematical model is as follows:
y(t)＝vmin+(vmax-vmin)
wherein, the random number is between 0 and 1.
Telemetry variables having threshold-related characteristics are typically voltage, current, temperature, pressure, etc. of the load. If the design value of the telemetering variable 'bus current' belongs to an interval [0, 5], namely the design value of the bus current is 0A at minimum and 5A at maximum.
3) Incremental enumeration association
Known enumeration set VEnu＝{v1，…，vnAny two adjacent simulation time ti、ti-1Value Y (t) of lower telemetry variable Yi)、y(ti-1) All have y (t)i)-y(ti-1) Belong to the set VEnuI.e., enumerate associations for increments. The mathematical model is as follows:
y(ti)-y(ti-1)＝α1v1+α2v2+…+αnvn
wherein the content of the first and second substances,
and alpha isi∈{0，1}。
Telemetry variables having incremental enumeration-associated characteristics typically include bus current, transmission frame count, number of timing, number of bus data broadcasts, and the like. If before and after each load device is powered on, the telemetering variable bus current is respectively increased by 0.3A, 0.5A and the like; the telemetry variable "GPS request count" is incremented at 4s intervalsThe value is constantly 8, etc. When the increment enumerates the associated telemetry variable simulation, the initial value y (t) needs to be specified0)。
4) Periodic correlation
Knowing the telemetry variable Y, set at simulation time t0，…,tlThe set of values under is { y (t) }0)，…，y(tl) If y (t)i+l)＝y(ti) Where i ∈ [0, l ]]Then the telemetry variable Y has a period-related characteristic. The mathematical model is as follows:
y(ti+nl)＝y(ti)
wherein l is the period of the telemetric variable and is a constant value, and n is an integer.
Telemetry variables with cycle correlation characteristics generally include: attitude parameters, orbit parameters, solar array power supply and other parameters associated with the satellite orbit cycle, and telemetry parameters when the same event and test are repeated for multiple times. For example, a certain satellite telemetry variable "Z-direction of J2000 satellite velocity" has a periodic characteristic of about 5400 seconds, which approximately corresponds to the orbital period of satellite operation. When the telemetry variable associated with the period is simulated, an initial simulation value in the first period needs to be specified.
5) Trend correlation
At the simulation time t, the values Y (t) of the telemetry variable Y are in a linear increasing, decreasing and invariant state, namely trend correlation. The data model is as follows:
y(t)＝kt+y(t0)
wherein y (t)0) For the initial value of the simulation, at k>0、k<0. And k is 0, the telemetric variable Y has the associated characteristics of linear increment, decrement or invariance respectively.
Telemetry variables that typically characterize the timing of operation of on-board equipment have trend-related characteristics. Such as the load telemetry variable "bus timecode broadcast count" exhibits a characteristic of a linear increasing trend. The trend correlation is sometimes characterized in a stepwise manner, for example, after the load is started, the temperature telemetric variable shows a trend of continuous temperature rise in a certain period of time, and the like. When the trend-associated telemetering variable is simulated, the initial simulation value y (t) is also required to be specified0)。
2. Telemetry data simulation submodule based on multi-telemetry variable association knowledge
Knowledge of the association of multiple telemetry variables means that the telemetry variable Y1，…，YnValue y at the same simulation time t1(t),…,yn(t) or values at different simulation moments. When the content of the telemetering data is simulated under the condition of multi-telemetering variable association, the simulation values of partial variables are determined firstly, and the simulation values of the rest variables are determined under the constraint of the association relationship.
1) Linear correlation
The mathematical model of the linear correlation is: knowing n telemetric variables Y1，…，YnValue y at simulation time t1(t)，…，yn(t) n numbers k not all equal to 01，…，knSo that:
the linear correlation often reflects the coupling design, method design of the load device. If a load telemetering variable 'data sending quantity' and a load manager telemetering variable 'data receiving quantity' present a complete and positive correlation linear relation, the relation is consistent with the design of communication interfaces of two devices; as another example, the telemetry variable "number of remaining events of the event table" and "number of executed events" tend to exhibit a completely inversely related linear relationship, consistent with the load manager event table execution mechanism design.
2) Monotonic association
Monotonic association in particular for two telemetric variables Y1，Y2The association relationship between them. The mathematical model is as follows: any two simulation times ti、tjTelemetric variable Y of the next two variables1，Y2Are each y1(ti)、y1(tj)、y2(ti)、y2(tj) There are always:
If it is
If the measured value is greater than 0, the two telemetric variables are in monotonic positive correlation; if it is
Less than 0, the two telemetry variables are monotonically negatively correlated.
3) Function association
The mathematical model of the functional association is: for n telemetry variables Y1,…,YnValue y at simulation time t1(t),…,yn(t) having:
yn(t)＝f(y1(t),…,yn-1(t))
physical factors such as space environment or load intrinsic working mechanism are the main reasons of telemetric variable function association. For example, the telemetered variable "solar cell array performance" is related to telemetered variables such as "solar illumination intensity", "incident angle", "operating temperature", "particle irradiation dose", and "engine plume contamination"; under an inertial coordinate system, telemetering variables of the X direction of the current orbit position and the X direction of the current orbit satellite speed are approximate to a trigonometric function relation and the like.
4) Delay function correlation
Delay function correlation means that the effect of one telemetry variable on another telemetry variable is not instantaneous and must be manifested after a period of time. The mathematical model is described as follows: telemetric variable Y1At simulation time tiIs given as y1(ti) Remote measuring of variable Y2At simulation time tjIs given as y2(tj) The method comprises the following steps:
y2(tj)＝f(y1(ti))
wherein t isj>ti。
The correlation of the delay function is also a reflection of the design of the satellite-borne equipment. For example, the change rule of the temperature telemetering variables corresponding to two temperature patches which are positioned inside the payload and installed on the outer surface of the payload is delayed for a period of time, and the temperature telemetering performance of the external patch generally lags behind that of the internal patch of the payload.
3. Telemetry data simulation submodule based on injection instruction and telemetry variable association knowledge
The injection instruction and the telemetering variable association knowledge means that the telemetering variable of the satellite-borne equipment changes correspondingly under the excitation of the injection instruction.
1) Interval association
The interval correlation model is: t after load execution instruction cBAt that moment, its telemetric variable Y takes the value Y (c, t)B) Will change to a certain interval VThr＝[vmin,vmax]. The model is described as follows:
y(c,tB)＝vmin+(vmax-vmin)
which is a random number between 0 and 1.
Load voltage and current value telemetry tends to satisfy the nature of interval correlation. If the load controller master is powered on after the injection instruction, the load controller master 3.3V voltage is telemetrically changed to the interval [2.8,3.8 ].
2) Numerical association
The numerical correlation model is: t after load execution instruction cBAt that moment, its telemetric variable Y takes the value Y (c, t)B) Will change to a constant value vcon. The model is described as follows:
y(c,tB)＝vcon
numerical associations are common in boolean telemetry variables or enumerated telemetry variables associated with instructions. If the injection instruction 'high-resolution camera master share' is executed, bit1 of a 'load on/off state' telemetering variable is changed into 1; after the injection instruction "the topographic camera is set to the dynamic camera mode" is executed, "the topographic camera device operation mode" telemetric variable value becomes 0x02, and so on.
3) Incremental numerical association
The incremental value is associated with a model before and after the load executes the command c (corresponding to the time t)F、tB) The difference Y (c, t) between the values of the telemetric variable YB)-y(c,tF) Is a constant value vconNamely:
y(c，tB)＝y(c，tF)+vcon
the telemetry variable "load injection count" typically satisfies this correlation characteristic. After the injection instruction 'CMOS image output request' is executed, the telemetry variable 'data injection packet reception count' is fixedly incremented by 1.
4) Incremental interval association
The model of increment interval association is: before and after the load executes the injection command c (corresponding to the time t)F、tB) The difference between the values of the telemetric variable Y belongs to the interval VThr＝[vmin,vmax]. The model is described as follows:
y(c,tB)＝y(c,tF)+vmin+(vmax-vmin)
which is a random number between 0 and 1.
If the remote control command 'the start of the topographic camera' is executed, the increment of the telemetering variable 'bus current' belongs to the interval [0.28,0.32 ].
4. Telemetry data simulation submodule based on health management strategy and telemetry variable association knowledge
The state of the load equipment is autonomously monitored in consideration of high cost and long time delay of ground ultra-long distance load control, the load equipment is autonomously controlled according to a health management strategy, and the method is one of key technologies which must be broken through in a subsequent deep space exploration task. The remote measurement variable reflects the state of the satellite borne equipment, and the execution of the remote control command is a main way for the satellite borne equipment to autonomously control. Therefore, the association of the health management policy with the telemetry variable refers to the association of the telemetry variable value and the event of "remote command sending".
1) Continuous threshold association
The continuous threshold correlation model is: and after the value of the remote measurement variable Y continuously exceeds the threshold value for n times, executing a correlation instruction c, wherein the model is designed as follows:
wherein v isthrIs a threshold value.
If the monitoring remote measuring variable 'load complete machine working current' exceeds the limit for 3 times, the load manager automatically executes a 'load equipment power-off' instruction; after monitoring that the telemetering variable load temperature exceeds the limit for 2 times, the load manager automatically executes a load shutdown command.
2) Multidimensional incremental interval correlation
The multidimensional increment interval correlation model is as follows: two telemetering variables Y acquired at a certain simulation t moment1,Y2Value y1(t)、y2(t) the difference belongs to a certain interval VThr＝[vmin，vmax]Instruction c is executed.
y1(t)＝y2(t)+vmin+(vmax-vmin)→c
Which is a random number between 0 and 1.
If the difference between the telemetering values of the lens temperature and the electric control box temperature of the resolution camera in the load reaches 10, the injection command of closing the corresponding heater is automatically executed, and if the difference is less than or equal to 9, the injection command of opening the corresponding heater is automatically executed.
5. Telemetry data simulation submodule based on correlation knowledge of fault and telemetry variable
When the telemetering data shows a certain characteristic, the telemetering data often means that a certain fault occurs in the satellite-borne equipment. And (3) the correlation between the fault and the telemetering variable is the correlation between the telemetering variable value characteristic and the event of 'fault'.
The associated knowledge of the univariate telemetry, the multivariate telemetry, the injection instruction and the health management strategy reflects the design of the satellite-borne equipment, and if the simulation data does not have the associated characteristics, the fault is indicated.
The method is successfully applied to the key scientific problems of the lunar scientific research station and the advanced research projects of the key technologies of the effective load. Application results show that the load telemetering data generated by the payload semi-physical simulation system and the load manager semi-physical simulation system constructed based on the method can reflect the working mechanism and the dynamic working process of load equipment; by full-virtual integration of the two semi-physical simulation systems or semi-virtual integration of the payload semi-physical simulation system and a real load manager prototype, the load verification baseline can be effectively moved forward to the design stage, and the integration and test efficiency of the payload is remarkably improved.
Autonomous control and health management are the most typical characteristics of follow-up tasks in the fourth month of exploration and tasks of unattended lunar scientific research stations. The simulated payload telemetering data can effectively reflect the health management strategy and the autonomous control strategy of the satellite-borne equipment, and the method is expected to be widely verified and applied to engineering in the subsequent lunar exploration real task.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A payload semi-physical simulation system based on associative knowledge, the system comprising: the system comprises a telemetry data content simulation module, a telemetry data format simulation module, a load physical interface simulation module and a simulation platform module; wherein the content of the first and second substances,
the remote measuring data content simulation module is used for dynamically generating remote measuring original data which accord with the current working state of load equipment according to the received data injection instruction, the remote control instruction, the effective load remote measuring data association knowledge model and the load user test requirement;
the telemetering data format simulation module is used for carrying out source packet format packaging on telemetering original data based on a CCSDS protocol standard to generate load telemetering source packet data;
the load physical interface simulation module is used for sending load telemetering source packet data to the tested equipment through the satellite bus physical board card and receiving a data injection instruction, a remote control instruction and broadcast time code information sent by the tested equipment;
and the simulation platform module is used for managing the telemetry data content simulation module, the telemetry data format simulation module and the load physical interface simulation module to complete data exchange and matching cooperation among the modules.
2. The associative knowledge-based payload semi-physical simulation system according to claim 1, wherein the telemetry data content simulation module comprises: the system comprises a single telemetering variable associated knowledge simulation sub-module, a multi-telemetering variable associated knowledge simulation sub-module, an injection instruction and telemetering variable associated knowledge simulation sub-module, a health management strategy and telemetering variable associated knowledge simulation sub-module and a load fault and telemetering variable associated knowledge simulation sub-module; wherein the content of the first and second substances,
the single telemetering variable association knowledge simulation submodule is used for generating telemetering original data reflecting the association characteristics of the single telemetering variable according to an association knowledge model between values of the single telemetering variable at different moments;
the multi-telemetering variable association knowledge simulation submodule is used for generating telemetering original data reflecting the association characteristics of the multi-telemetering variables according to an association knowledge model of the plurality of telemetering variables between values at the same time or different times;
the injection instruction and telemetering variable association knowledge simulation submodule is used for simulating a telemetering variable which changes correspondingly under the excitation of an injection instruction and generating telemetering original data reflecting the execution state of a load instruction;
the health management strategy and telemetering variable association knowledge simulation submodule is used for generating telemetering original data triggering the load health management strategy according to an association knowledge model between the health management strategy and the telemetering variable;
and the load fault and telemetering variable association knowledge simulation submodule is used for generating telemetering original data triggering the load fault according to the association knowledge model between the fault and the telemetering variable.
3. The associated knowledge-based payload semi-physical simulation system of claim 2, wherein the single telemetric variable associated knowledge simulation submodule is implemented in a specific manner as follows:
when the values of the telemetering variable Y to be simulated at different simulation moments t all belong to the enumerated value set VEnu＝{v1，…，vnIn the time of the previous step, generating telemetry raw data y (t) by adopting an enumeration association model:
y(t)＝α1v1+α2v2+…+αnvn
wherein alpha isiThe number of the coefficients is represented by,
and alpha isi∈{0，1}；
When the values of the remote measurement variable Y to be simulated at different simulation moments t all belong to a threshold value interval VThr＝[vmin，vmax]Generating telemetry raw data y (t) by using a threshold correlation model:
y(t)＝vmin+(vmax-vmin)
wherein, is a random number between 0 and 1, vmin,vmaxRespectively the minimum value and the maximum value of the threshold interval;
when the remote measuring variable Y to be simulated is at two adjacent simulation moments ti-1、tiAll the increments of the values belong to an enumeration set VEnu＝{v1,…,vnAt time, from Y at ti-1Value y (t) of simulation timei-1) Generating telemetry raw data y (t) by using an incremental enumeration association model:
y(t)＝{y(t0),…,y(ti-1)，y(ti)}
y(ti)-y(ti-1)＝α1v1+α2v2+…+αnvn
wherein, y (t)0) For an initial value of simulation, alphaiAs a function of the number of the coefficients,
and alpha isi∈{0,1}；
When the value of the telemetering variable Y to be simulated at different simulation moments t presents the correlation characteristics of the period l, generating telemetering original data Y (t) by adopting a period correlation model:
y(t)＝{y(t0),…,y(tl)}
y(ti+nl)＝y(ti)
wherein, the integer i belongs to [0, l ], l is the period of the value of the telemetering variable, and n is an integer;
when the values of the telemetering variable Y to be simulated at different simulation moments t have the correlation characteristics of linear increment, decrement or invariance, generating telemetering original data Y (t) by adopting a trend correlation model:
y(t)＝kt+y(t0)
where k is the slope of a linear increment or decrement, y (t)0) Is an initial value of simulation.
4. The associated knowledge-based payload hardware-in-the-loop simulation system of claim 2, wherein the multi-telemetry variable associated knowledge simulation submodule is implemented by the following specific processes:
when n telemetric variables Y to be simulated1，…,YnWhen the linear correlation characteristic is provided, the linear correlation characteristic is obtained,
telemetering variable Y from the first n-11,…,Yn-1Value y at simulation time t1(t),…,yn-1(t) generating Y by a function correlation modelnTelemetry raw data y at simulation time tn(t)：
Wherein k is1,…,knN numbers not all being 0;
when the remote measurement variable Y is to be simulated2With another telemetric variable Y1When having a monotone correlation characteristic, by Y1At simulation time ti、tjValue y of1(ti)、y1(tj) Generating Y using a monotonic correlation model2At simulation time ti、tjLower telemetric raw data value y2(ti) And y2(tj)：
Wherein the content of the first and second substances,
greater than 0 represents Y1And Y2In order to be a monotonically positive association,
less than 0 represents Y1And Y2Is a monotonically negative association;
when the remote measurement variable Y is to be simulatednAnd n-1 telemetric variables Y1,…,Yn-1When having the function-related feature, is represented by Y1，…,Yn-1Value y of1(t),…,yn-1(t) generating Y by a function correlation modelnTelemetry raw data y at simulation time tn(t)：
yn(t)＝f(y1(t),…,yn-1(t))
Wherein f represents n telemetry variables Y1,…,YnFunctional relationship between;
when a telemetric variable Y1Remote measurement variable Y to be simulated2The effect of (2) is at a timeWhen it occurs later, from Y1At simulation time tiValue y of1(ti) Generating Y by using a time delay function correlation model2At simulation time tjRaw data y of telemetry data2(tj)：
y2(tj)＝f(y1(ti))
Wherein, tj>ti。
5. The associated knowledge-based payload hardware-in-the-loop simulation system of claim 2, wherein the injection instruction and telemetry variable associated knowledge simulation submodule is implemented by the following specific processes:
t after the load execution instruction c of the telemetric variable Y to be simulatedBAt that moment, the value of Y will change to the threshold interval VThr＝[vmin，vmax]Generating Y after the load execution instruction c t by using the interval correlation modelBTelemetric raw data y (c, t) at timeB)：
y(c，tB)＝vmin+(vmax-vmin)
Wherein is a random number between 0 and 1;
t after the load execution instruction c of the telemetric variable Y to be simulatedBAt that moment, the value of Y will change to a constant value vconGenerating Y by using a numerical correlation model after the load executes the instruction c and tBTelemetric raw data y (c, t) at timeB)：
y(c，tB)＝vcon；
When the remote measurement variable Y to be simulated is before and after the load execution instruction c, the difference between the values of the Y is a constant value vconThen instruction c is executed by Y before tFThe value y (c, t) of timeF) Generating Y after the load execution instruction c t by adopting an incremental numerical value correlation modelBTelemetric raw data y (c, t) at timeB)：
y(c，tB)＝y(c，tF)+vcon；
When the remote measurement variable Y is to be simulatedBefore and after the load executes the instruction c, the difference of the values of Y belongs to a threshold interval VThr＝[vmin,vmax]Then instruction c is executed by Y before tFThe value y (c, t) of timeF) Generating T after load execution instruction c of Y by adopting increment interval association modelBTelemetric raw data y (c, t) at timeB)：
y(c，tB)＝y(c，tF)+vmin+(vmax-vmin)
Wherein, the random number is between 0 and 1.
6. The associated knowledge-based payload hardware-in-the-loop simulation system of claim 2, wherein the implementation of the health management strategy and telemetry variable associated knowledge simulation sub-module comprises: a continuous threshold correlation model and a multi-dimensional increment interval correlation model; wherein the content of the first and second substances,
when the value of the remote measurement variable Y to be simulated exceeds the threshold value for n times continuously, the correlation instruction c is automatically triggered to be executed, the remote measurement original data Y (t) of the Y is generated by adopting a continuous threshold value correlation model, and the n times are all larger than the threshold value;
when the remote measurement variable Y is to be simulated1The value corresponding to the simulation time t and another telemetering variable Y2The difference value of the corresponding values at the simulation time t belongs to a threshold interval VThr＝[vmin，vmax]Automatically triggering execution of the associated command c, then Y2Value y of2(t) generating Y by using a multidimensional incremental interval correlation model1Telemetering raw data y1(t)：
y1(t)＝vmin+(vmax-vmin)+y2(t)
Wherein, the random number is between 0 and 1.
7. The associated knowledge-based payload semi-physical simulation system according to claim 2, wherein the load fault and telemetry variable associated knowledge simulation submodule is implemented in a specific manner as follows:
and modifying the telemetering original data generated by the single telemetering variable associated knowledge simulation submodule, the multi-telemetering variable associated knowledge simulation submodule, the injection instruction and telemetering variable associated knowledge simulation submodule and the health management strategy and telemetering variable associated knowledge simulation submodule to ensure that the modified telemetering original data does not meet the original associated knowledge model, thereby obtaining the simulated fault telemetering original data.
8. The associated knowledge based payload semi-physical simulation system of claim 1, wherein bus interfaces between the payload physical interface simulation module and the satellite bus physical board comprise a 1553B bus interface, a CAN bus interface and an RS422 bus interface.
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